Asthma affects 5% of the world population. In the U.S., asthma death rates are four-fold higher in Latinos and African Americans compared to Whites. Latinos and African Americans have varying degrees of African, European, and Native American genetic ancestry. This genetic heterogeneity has important clinical implications. In fact, we demonstrated that we can improve the diagnosis of lung disease among African Americans by as much as 15% by including genetic measures of ancestry into clinical lung function prediction equations (NEJM). We improved upon our results by incorporating sub-continental Native American ancestry into clinical lung function prediction equations for Latinos (Science). We also demonstrated that exposure to air pollution is associated with increased asthma risk among Latino and African American children, and that this increase in risk was greatest among African American children (AJRCCM). Subsequently, we demonstrated that environmental and social risk factors cannot fully explain the correlation between genetic ancestry and asthma severity and lung function (JACI). Clearly, we demonstrated that ancestry plays a strong role in determining normal clinical measures such as lung function and asthma severity. We hypothesize that gene- environment interactions contribute to population differences in lung function and asthma severity following exposure to air pollution. Racial/ethnic differences in the frequency and composition of genetic variation likely play an important role in asthma severity. To test this hypothesis we recruited the largest gene-environment study of asthma among minority children in the U.S. (N > 10,000). Our goal is to use integrative genomics to identify gene-by-air pollution interactions that influence lung function and asthma severity in minority children. We will leverage existing 1,600 whole genome sequences with air pollution-induced gene expression in nasal airway epithelial cells (NECs) from children with mild and severe asthma. We will integrate individual-level whole genome sequencing data with precise cell-level genetic and exposure-response experiments. We will identify genetic risk factors and gene-environment interactions that contribute to asthma severity, and determine cellular response mechanisms that mediate poor lung function and severe asthma using NECs exposed to air pollution.